A technology is known for detecting feature points of an object that is captured in an input image. For example, from the face of a person captured in an input image, feature points such as the eyes, the nose, and the mouth are detected. Examples of such a feature point detection technology include the following technology.
For example, consider a case of detecting D (D≧1) number of true feature points of an object that is captured in an input image. In that case, D (D≧1) number of initial feature points are set in the object captured in the input image. Herein, the initial feature points correspond to the D number of true feature points.
Then, each of T (T≧1) number of K-class (K≧2) classifiers classifies the input image in one of K-classes, and outputs a displacement vector of the corresponding class. Herein, the K-class classifiers are classifiers learnt in advance using training (learning) samples in such a way that similar image patterns with respect to the coordinates of initial feature points are classified in the same class. The displacement vector of each class is a vector calculated in such a way that the coordinates of initial feature points of the training samples classified in the corresponding class are approximated to the coordinates of true feature points.
Lastly, T number of displacement-vectors output from the T number of K-class classifiers are sequentially added to an initial feature point vector consisting of the D number of initial feature points of an input image, and the initial feature point vector is sequentially updated. As a result, the D number of initial feature points are asymptotically approximated to the true feature points, and the D number of true feature points are detected.
In the conventional technology described above, it is necessary to store K×T number of displacement vectors representing high-dimensional data. That is, it is necessary to store a large amount of displacement vectors representing high-dimensional data. That leads to an increase in the required memory capacity.